Mining usage patterns in residential intranet of things

Abstract : Ubiquitous smart technologies gradually transform modern homes into Intranet of Things, where a multitude of connected devices allow for novel home automation services (e.g., energy or bandwidth savings, comfort enhancement, etc.). Optimizing and enriching the Quality of Experience (QoE) of residential users emerges as a critical differentiator for Internet and Communication Service providers (ISPs and CSPs, respectively) and heavily relies on the analysis of various kinds of data (connectivity, performance , usage) gathered from home networks. In this paper, we are interested in new Machine-to-Machine data analysis techniques that go beyond binary association rule mining for traditional market basket analysis considered by previous works, to analyze individual device logs of home gateways. Based on multidimensional patterns mining framework, we extract complex device co-usage patterns of 201 residential broadband users of an ISP, subscribed to a triple-play service. Such fine-grained device usage patterns provide valuable insights for emerging use cases such as an adaptive usage of home devices, and also " things " recommendation.
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The 7th International Conference on Ambient Systems, Networks and Technologies, May 2016, Madrid Spain. The 7th International Conference on Ambient Systems, Networks and Technologies (ANT 2016) / The 6th International Conference on Sustainable Energy Information Technology (SEIT-2016) / Affiliated Workshops, 00, pp.6, 2016, International Workshop on Big Data and Data Mining Challenges on IoT and Pervasive Systems (BigD2M 2016) 〈http://cs-conferences.acadiau.ca/ant-16/〉
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Gevorg Poghosyan, Ioannis Pefkianakis, Pascal Le Guyadec, Vassilis Christophides. Mining usage patterns in residential intranet of things. The 7th International Conference on Ambient Systems, Networks and Technologies, May 2016, Madrid Spain. The 7th International Conference on Ambient Systems, Networks and Technologies (ANT 2016) / The 6th International Conference on Sustainable Energy Information Technology (SEIT-2016) / Affiliated Workshops, 00, pp.6, 2016, International Workshop on Big Data and Data Mining Challenges on IoT and Pervasive Systems (BigD2M 2016) 〈http://cs-conferences.acadiau.ca/ant-16/〉. 〈hal-01411676〉

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